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• Human-Centered Design for Learner Acceptance, Engagement, and Performance
• Design Strategies and Guidelines for Adaptive Instructional Systems
• Individual Differences in Adaptive Learning
• Applications of Adaptive Instructional Systems

This book constitutes the refereed proceedings of the 5th International Conference, AIS 2023, held as part of the 25th International Conference, HCI International 2023, which was held virtually in Copenhagen, Denmark in July 2023. The total of 1578 papers and 396 posters included in the HCII 2023 proceedings was carefully reviewed and selected from 7472 submissions. The AIS 2023 proceeding helps to understand the theory and enhance the state-of-practice for a set of technologies (tools and methods) called adaptive instructional systems (AIS). AIS are defined as artificially intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, preferences, and interests of each individual learner or team in the context of domain learning objectives

• Intro
• Preface
• Workshop Description
• Organization
• Causal Discovery in Observational Time Series (Invited Talk)
• Contents
• Oral Presentation
• Adjustable Context-Aware Transformer
• 1 Introduction
• 2 Problem Definition
• 3 Related Work
• 4 Methodology
• 4.1 Background: Issues Arising from Point-Wise Attention
• 4.2 Temporal Attention
• 4.3 Adjustable Context-Aware Attention
• 4.4 Efficient Adjustable Context-Aware Attention
• 4.5 Overarching Architecture
• 5 Experiments
• 5.1 Datasets
• 5.2 Evaluation Metrics
• 5.3 Baselines

• 5.4 Model Training and Hyperprarameters
• 5.5 Results and Discussion
• 6 Conclusion
• References
• Clustering of Time Series Based on Forecasting Performance of Global Models
• 1 Introduction
• 2 A Clustering Algorithm Based on Prediction Accuracy of Global Forecasting Models
• 3 Simulation Study
• 3.1 Experimental Design
• 3.2 Alternative Approaches and Assessment Criteria
• 3.3 Results and Discussion
• 4 Application to Real Data
• 5 Conclusions
• References
• Experimental Study of Time Series Forecasting Methods for Groundwater Level Prediction
• 1 Introduction

• 2 Data Collection
• 3 Groundwater Level Forecasting
• 3.1 Local Versus Global Time Series Forecasting
• 3.2 Considered Methods
• 4 Experimental Settings
• 4.1 Setup
• 4.2 Comparison Metrics
• 5 Results
• 5.1 Generalized Autoregressive Models Results
• 5.2 DeepAR-Based Models Results
• 5.3 Prophet-Based Models Results
• 5.4 Comparing the Three Groups of Models
• 5.5 Discussion
• 6 Conclusion
• References
• Fast Time Series Classification with Random Symbolic Subsequences
• 1 Introduction
• 2 Related Work
• 3 Proposed Method
• 3.1 Symbolic Representations of Time Series

• 3.2 MrSQM Variants
• 4 Evaluation
• 4.1 Experiment Setup
• 4.2 Sensitivity Analysis
• 4.3 MrSQM Versus State-of-the-Art Symbolic Time Series Classifiers
• 4.4 MrSQM Versus Other State-of-the-Art Time Series Classifiers
• 5 Conclusion
• References
• RESIST: Robust Transformer for Unsupervised Time Series Anomaly Detection
• 1 Introduction
• 2 Related Work
• 3 Method
• 3.1 RESIST Architecture
• 3.2 Robust Training Loss
• 3.3 Hypotheses
• 4 Experiments and Results
• 4.1 Dataset Description
• 4.2 Data Preprocessing
• 4.3 Training and Testing Protocols

• 4.4 Training Parameter Settings and Evaluation Criteria
• 4.5 Results
• 5 Conclusion and Perspectives
• References
• Window Size Selection in Unsupervised Time Series Analytics: A Review and Benchmark
• 1 Introduction
• 2 Background and Related Work
• 2.1 Definitions
• 2.2 Anomaly Detection
• 2.3 Segmentation
• 2.4 Motif Discovery
• 3 Window Size Selection
• 3.1 Dominant Fourier Frequency
• 3.2 Highest Autocorrelation
• 3.3 Hybrids: AutoPeriod and RobustPeriod
• 3.4 Multi-Window-Finder
• 3.5 Summary Statistics Subsequence
• 4 Experimental Evaluation
• 4.1 Setup

This book constitutes the refereed proceedings of the 7th ECML PKDD Workshop, AALTD 2022, held in Grenoble, France, during September 1923, 2022. The 12 full papers included in this book were carefully reviewed and selected from 21 submissions. They were organized in topical sections as follows: Oral presentation and poster presentation.

This book constitutes the proceedings of the 35th International Conference on Advanced Information Systems Engineering, CAiSE 2023, which was held in Zaragoza, Spain, during June 12-16, 2023. The 36 full papers included in these proceedings were selected from 161 submissions. They were organized in topical sections as follows: Cyber-human and cyber-physical systems; requirements engineering; IoT; environmental applications; process mining; event-driven process mining; ontology and knowledge representation; model-driven approaches; process monitoring; conformance, compliance and workarounds; data-centric approaches; privacy and security; explainable AI; service-related approaches. .

• Classification and Case-Based Reasoning
• Computer Vision
• Decision Making
• E-Learning
• Information Fusion
• Knowledge Graph and Link Prediction
• Machine Learning Theory
• Pattern Recognition
• Industrial Applications
• Natural Language Processing
• Optimization
• Prediction
• Reinforcement Learning
• Security
• Various Applications

This double volume LNAI 13925-13926 constitutes the thoroughly refereed proceedings of the 36th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2023, held in Shanghai, China, in July 2023. The 50 full papers and 20 short papers presented were carefully reviewed and selected from 129 submissions. The IEA/AIE 2023 conference on applications of applied intelligent systems to solve real-life problems in all areas including business and finance, science, engineering, industry, cyberspace, bioinformatics, automation, robotics, medicine and biomedicine, and human-machine interactions

• IMAGE ANALYSIS & PROCESSING.- Multi-granularity feature attention Fusion network for Image-Text Sentiment Analysis.- Toward efficient image denoising: A lightweight network with retargeting supervision driven knowledge distillation.- A pig pose estimation model for measuring pig's body size.- Topology-aware Learning for Semi-supervised Cross-domain Retinal Artery/Vein Classification.- Face Super-Resolution with Better Semantics and More Efficient Guidance.- GRAPHS & NETWORKS.- Layout and Display of Network Graphs on a Sphere.- Joint Matrix Factorization and Structure Preserving for Domain Adaptation.- Graph Adversarial Network with Bottleneck Adapter Tuning for Sign Language Production.- ESTIMATION & FEATURE MATCHING.- Facial Landmarks based Region-Level Data Augmentation for Gaze Estimation.- An Efficient Dense Depth Map Estimation Algorithm Using Direct Stereo Matching For Ultra-wide-angle Images.- Ad-RMS: Adaptive Regional Motion Statistics for Feature Matching Filtering.- 3D RECONSTRUCTION.- Mobile Augmented Realty Based Visual Indoor Navigation.- Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view Stereo.- Reconstructing the Surface Mesh Representation for Single Neuron.- WEmap: Weakness-enhancement mapping for 3D reconstruction with sparse image sequences.- RENDERING & ANIMATION.- Comparing Traditional Rendering Techniques to Deep Learning based Super-Resolution in Fire and Smoke Animations.- Real-Time Light Field Path Tracing.- Crowd Simulation with Detailed Body Motion and Interaction.- Towards Rendering the Style of 20th Century Cartoon Line Art in 3D Real-time.- DETECTION & RECOGNITION.- Face detection algorithm in classroom scene based on deep learning.- GRVT: Toward Effective Grocery Recognition via Vision Transformer.- A Transformer-based Cloth-irrelevant Patches Feature Extracting Method for Long-Term Cloth-Changing Person Re-identification.- Learning Unified Binary Feature Codes For Cross-illumination Palmprint Recognition.- COLORS, PAINTINGS & LAYOUT.- SemiPainter: Learning to draw semi-realistic paintings from the manga line drawings and flat shadow.- Hierarchical Bayesian Network Modeling and Layout of Huizhou Traditional Villages in Geographic Environment.- AE-GAN: Attention Embedded GAN For Irregular and Large-area Mask Face Image Inpainting.- SYNTHESIS & GENERATION.- Procedural Generation of Landscapes with Water Bodies Using Artificial Drainage Basins.- High-fidelity Dynamic Human Synthesis via UV-guided NeRF with Sparse Views.- Rec2Real: Semantics-Guided Photo-Realistic Image Synthesis Using Rough Urban Reconstruction Models.- 3D Digital City Structure Model Based on Image Modeling Technology.- AR & USER INTERFACES.- Augmented Reality-Based Home Interaction Layout and Evaluation.- LiteAR: A Framework to Estimate Lighting for Mixed Reality Sessions for Enhanced Realism.- Personalized User Interface Elements Recommendation System.- MEDICAL IMAGING.- A feature point extraction method for capsule endoscope localization.- Automated Diagnosis Of Retinal Neovascularization Pathologies From Color Retinal Fundus Images.- SEGMENTATION.- DDCNet: A lightweight network with variable receptive field for real-time portrait segmentation in complex environment.- A chromosome segmentation method based on corner detection and watershed algorithm.- Voxel-based 3D shape segmentation using deep volumetric convolutional neural networks.- OBJECT DETECTION.- Few-shot detection based on an enhanced prototype for outdoor small forbidden objects.- Research on real-time forestry pest detection based on improved YOLOv5.- Power Line Detection Based on Feature Fusion Deep Learning Network.- IMAGE ATTENTION & PERCEPTION.- Wider and Higher: Intensive Integration and Global Foreground Perception for Image Matting.- Authenticity Identification of Qi Baishi's Shrimp Painting with Dynamic Token Enhanced Visual Transformer.- MODELING & SIMULATION.- An Optimized Material Point Method for Soil-Water Coupled Simulation.- SlimFliud-Net: Fast Fluid Simulation Using Admm Pruning. .
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 39th Computer Graphics International Conference on Advances in Computer Graphics, CGI 2022, held Virtually, during September 12-16, 2022. The 45 full papers included in this book were carefully reviewed and selected from 139 submissions. They were organized in topical sections as follows: image analysis & processing; graphs & networks; estimation & feature matching; 3d reconstruction; rendering & animation; detection & recognition; colors, paintings & layout; synthesis & generation; ar & user interfaces; medical imaging; segmentation; object detection; image attention & perception; and modeling & simulation.
(source: Nielsen Book Data)

• Towards a Model-driven Approach for Big Data Analytics in the Genomics Field.- Conceptual Modeling-based Cardiopathies Data Management.- A Conceptual Model of Health Monitoring Systems Centered on ADLs Performance in Older Adults.- A Comparative Analysis of the completeness and concordance of data sources with cancer-associated information.- A Flexible Automated Pipeline Engine for Transcript-level Quantification from RNA-seq.- An Initial Empirical Assessment of an Ontological Model of the Human Genome.- Unsupervised factor extraction from pretrial detention decisions by Italian and Brazilian Supreme Courts.- Context-Aware Knowledge Extraction from Legal Documents through Zero-Shot Classification.- On the Lack of Legal Regulation in Conceptual Modeling Automated extraction and representation of citation network: a CJEU case-study.- A Rule 74 for Italian judges.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of three workshops symposia, held at the 41st International Conference on Conceptual Modeling, ER 2022, as a virtual event, in October 2022. The 11 papers deal with topics such as foundations; business information systems; advanced and cross-disciplinary applications, conceptual modeling, information systems and knowledge graphs; data/object management; engineering; and experience. The following workshops are included in this volume: Third International Workshop on Conceptual Modeling for Life Sciences (CMLS), 5th International Workshop on Empirical Methods in Conceptual Modeling (EmpER); and First International Workshop on Digital Justice, Digital Law and Conceptual Modeling (JUSMOD).
(source: Nielsen Book Data)

This five-volume set, LNCS 14004 - 14008 constitutes the refereed proceedings of the 42nd Annual International Conference on Theory and Applications of Cryptographic Techniques, Eurocrypt 2023, which was held in Lyon, France, in April 2023. The total of 109 full papers presented were carefully selected from 415 submissions. They are organized in topical sections as follows: Theoretical Foundations; Public Key Primitives with Advanced Functionalities; Classic Public Key Cryptography; Secure and Efficient Implementation, Cryptographic Engineering, and Real-World Cryptography; Symmetric Cryptology; and finally Multi-Party Computation and Zero-Knowledge.

This five-volume set, LNCS 14004 - 14008 constitutes the refereed proceedings of the 42nd Annual International Conference on Theory and Applications of Cryptographic Techniques, Eurocrypt 2023, which was held in Lyon, France, in April 2023. The total of 109 full papers presented were carefully selected from 415 submissions. They are organized in topical sections as follows: Theoretical Foundations; Public Key Primitives with Advanced Functionalities; Classic Public Key Cryptography; Secure and Efficient Implementation, Cryptographic Engineering, and Real-World Cryptography; Symmetric Cryptology; and finally Multi-Party Computation and Zero-Knowledge.

This five-volume set, LNCS 14004 - 14008 constitutes the refereed proceedings of the 42nd Annual International Conference on Theory and Applications of Cryptographic Techniques, Eurocrypt 2023, which was held in Lyon, France, in April 2023. The total of 109 full papers presented were carefully selected from 415 submissions. They are organized in topical sections as follows: Theoretical Foundations; Public Key Primitives with Advanced Functionalities; Classic Public Key Cryptography; Secure and Efficient Implementation, Cryptographic Engineering, and Real-World Cryptography; Symmetric Cryptology; and finally Multi-Party Computation and Zero-Knowledge.

This five-volume set, LNCS 14004 - 14008 constitutes the refereed proceedings of the 42nd Annual International Conference on Theory and Applications of Cryptographic Techniques, Eurocrypt 2023, which was held in Lyon, France, in April 2023. The total of 109 full papers presented were carefully selected from 415 submissions. They are organized in topical sections as follows: Theoretical Foundations; Public Key Primitives with Advanced Functionalities; Classic Public Key Cryptography; Secure and Efficient Implementation, Cryptographic Engineering, and Real-World Cryptography; Symmetric Cryptology; and finally Multi-Party Computation and Zero-Knowledge.

This five-volume set, LNCS 14004 - 14008 constitutes the refereed proceedings of the 42nd Annual International Conference on Theory and Applications of Cryptographic Techniques, Eurocrypt 2023, which was held in Lyon, France, in April 2023. The total of 109 full papers presented were carefully selected from 415 submissions. They are organized in topical sections as follows: Theoretical Foundations; Public Key Primitives with Advanced Functionalities; Classic Public Key Cryptography; Secure and Efficient Implementation, Cryptographic Engineering, and Real-World Cryptography; Symmetric Cryptology; and finally Multi-Party Computation and Zero-Knowledge.

• Full Papers
• Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item Retrieval
• User Requirement Analysis for a Recommender System-Based Meeting Assistant
• Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering
• Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation
• Injecting the BM25 Score as Text Improves BERT-Based Re-rankers
• Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformers
• A Knowledge Infusion based Multitasking System for Sarcasm Detection in Meme
• Multilingual Detection of Check-Worthy Claims using World Languages and Adapter Fusion
• Market-Aware Models for Efficient Cross Market Recommendation
• TourismNLG: A Multi-lingual Generative Benchmark for the Tourism Domain
• An Interpretable Knowledge Representation Framework for Natural Language Processing with Cross-Domain Application
• Graph-based Recommendation for Sparse and Heterogeneous User Interactions
• It’s Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers
• Recommendation Algorithm Based on Deep Light Graph Convolution Network in Knowledge Graph
• Query Performance Prediction for Neural IR: Are We There Yet?
• Item Graph Convolutional Collaborative Filtering for Inductive Recommendations
• CoLISA: Inner Interaction via Contrastive Learning for Multi-Choice Reading Comprehension
• Viewpoint Diversity in Search Results
• COILCR: Efficient Semantic Matching in Contextualized Exact Match Retrieval
• Bootstrapped nDCG Estimation in the Presence of Unjudged Documents
• Predicting the Listening Contexts of Music Playlists Using Knowledge Graphs
• Keyword Embeddings for Query Suggestion
• Domain-driven and Discourse-guided Scientific Summarisation
• Injecting Temporal-aware Knowledge in Historical Named Entity Recognition
• A Mask-based Logic Rules Dissemination Method for Sentiment Classifiers
• Contrasting Neural Click Models and Pointwise IPS Rankers
• Sentence Retrieval for Open-Ended Dialogue using Dual Contextual Modeling
• Temporal Natural Language Inference: Evidence-based Evaluation of Temporal Text Validity
• Theoretical Analysis on the Efficiency of Interleaved Comparisons
• Intention-aware Neural Networks for Question Paraphrase Identification
• Automatic and Analytical Field Weighting for Structured Document Retrieval
• An Experimental Study on Pretraining Transformers from Scratch for IR
• Neural Approaches to Multilingual Information Retrieval
• CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval
• SR-CoMbEr: Heterogeneous Network Embedding using Community Multi-view Enhanced Graph Convolutional Network for Automating Systematic Reviews
• Multimodal Inverse Cloze Task for Knowledge-based Visual Question Answering
• A Transformer-based Framework for POI-level Social Post Geolocation
• Document-Level Relation Extraction with Distance-dependent Bias Network and Neighbors Enhanced Loss
• Investigating Conversational Agent Action in Legal Case Retrieval
• MS-Shift: An Analysis of MS MARCO Distribution Shifts on Neural Retrieval
• Listwise Explanations for Ranking Models using Multiple Explainers
• Improving video retrieval using multilingual knowledge transfer
• Service is good, very good or excellent? Towards Aspect based Sentiment Intensity Analysis
• Effective Hierarchical Information Threading using Network Community Detection
• HADA: A Graph-based Amalgamation Framework in Image-Text Retrieval.

The three-volume set LNCS 13980, 13981 and 13982 constitutes the refereed proceedings of the 45th European Conference on IR Research, ECIR 2023, held in Dublin, Ireland, during April 2-6, 2023. The 65 full papers, 41 short papers, 19 demonstration papers, and 12 reproducibility papers, 10 doctoral consortium papers were carefully reviewed and selected from 489 submissions. The accepted papers cover the state of the art in information retrieval focusing on user aspects, system and foundational aspects, machine learning, applications, evaluation, new social and technical challenges, and other topics of direct or indirect relevance to search.

• Full Papers
• Automatic Summarization of Financial Earnings Calls Transcript
• Parameter-Efficient Sparse Retrievers and Rerankers using Adapters
• Feature Differentiation and Fusion for Semantic Text Matching
• Multivariate Powered Dirichlet-Hawkes Process
• Fragmented Visual Attention in Web Browsing: Weibull Analysis of Item Visit Times
• Topic-Enhanced Personalized Retrieval-based Chatbot
• Improving the Generalizability of the Dense Passage Retriever Using Generated Datasets
• SegmentCodeList: Unsupervised Representation Learning for Human Skeleton Data Retrieval
• Knowing What and How: A Multi-modal Aspect-Based Framework for Complaint Detection
• What is your cause for concern? Towards Interpretable Complaint Cause Analysis
• DeCoDE: DEtection of COgnitive Distortion and Emotion cause extraction in clinical conversations
• Domain-aligned Data Augmentation for Low-resource and Imbalanced Text Classification
• Privacy-Preserving Fair Item Ranking
• Multimodal Geolocation Estimation of News Photos
• Topics in Contextualised Attention Embeddings
• New Metrics to Encourage Innovation and Diversity in Information Retrieval Approaches
• Probing BERT for Ranking Abilities
• Clustering of Bandit with Frequency-Dependent Information Sharing
• Contrastive Graph Learning with Positional Representation for Recommendation
• Domain Adaptation for Anomaly Detection on Heterogeneous Graphs in E-Commerce
• Short Papers Improving Neural Topic Models with Wasserstein Knowledge Distillation
• Towards Effective Paraphrasing for Information Disguise
• Generating Topic Pages for Scientific Concepts Using Scientific Publications
• Relevance Judgements for Fair Ranking
• A Study of Term-Topic Embeddings for Ranking
• Topic Refinement in Multi-Level Hate Speech Detection
• Is Cross-modal Information Retrieval Possible without Training?
• Adversarial Adaptation for French Named Entity Recognition
• Exploring Fake News Detection with Heterogeneous Social Media Context Graphs
• Justifying Multi-Label Text Classifications for Healthcare Applications
• Doc2Query–: When Less is More
• Towards Quantifying The Privacy Of Redacted Text. -Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study
• Leveraging Comment Retrieval for Code Summarization
• CPR: Cross-domain Preference Ranking with User Transformation
• Colbert-FairPRF: Towards Fair Pseudo-Relevance Feedback in Dense Retrieval
• C2LIR: Continual Cross-lingual Transfer for Low-Resource Information Retrieval
• Joint Extraction and Classification of Danish Competences for Job Matching
• A Study on FGSM Adversarial Training for Neural Retrieval
• Dialogue-to-Video Retrieval
• Time-dependent next-basket recommendations
• Investigating the Impact of Query Representation on Medical Information Retrieval
• Where a Little Change Makes a Big Difference: A Preliminary Exploration of Children’s Queries
• Multi-document QA with GPT-3 and Neural Reranking
• Towards Detecting Interesting Ideas Expressed in Text
• Towards Linguistically Informed Multi-Objective Transformer Pre-Training for Natural Language Inference
• Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion Networks
• Consumer Health Question Answering Using Off-the-shelf Components
• MOO-CMDS+NER: Named Entity Recognition-based Extractive Comment-oriented Multi-document Summarization
• Don’t Raise Your Voice, Improve Your Argument: Learning to Retrieve Convincing Arguments
• Learning Query-Space Document Representations for High-Recall Retrieval
• Investigating Conversational Search Behavior For Domain Exploration
• Evaluating Humorous Response Generation to Playful Shopping Requests
• Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents
• Trigger or not Trigger: Dynamic Thresholding for Few Shot Event Detection
• The Impact of a Popularity Punishing Hyperparameter on ItemKNN Recommendation Performance
• Neural Ad hoc Retrieval Meets Information Extraction
• Augmenting Graph Convolutional Networks with Textual Data for Recommendations
• Utilising Twitter Metadata for Hate Classification
• Evolution of Filter Bubbles and Polarization in News Recommendation
• Capturing Cross-platform Interaction for Identifying Coordinated Accounts of Misinformation Campaigns.

The three-volume set LNCS 13980, 13981 and 13982 constitutes the refereed proceedings of the 45th European Conference on IR Research, ECIR 2023, held in Dublin, Ireland, during April 2-6, 2023. The 65 full papers, 41 short papers, 19 demonstration papers, 12 reproducibility papers consortium papers, 7 tutorial papers, and 10 doctorial consortium papers were carefully reviewed and selected from 489 submissions. The book also contains, 8 workshop summaries and 13 CLEF Lab descriptions. The accepted papers cover the state of the art in information retrieval focusing on user aspects, system and foundational aspects, machine learning, applications, evaluation, new social and technical challenges, and other topics of direct or indirect relevance to search.

• Reproducibility Papers
• Knowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation
• Stat-weight: Improving the Estimator of Interleaved Methods Outcomes with Statistical Hypothesis Testing
• A Reproducibility Study of Question Retrieval for Clarifying Questions
• The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer
• Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study
• Index-Based Batch Query Processing Revisited
• A Unified Framework for Learned Sparse Retrieval
• Entity Embeddings for Entity Ranking: A Replicability Study
• Do the Findings of Document and Passage Retrieval Generalize to the Retrieval of Responses for Dialogues?
• PyGaggle: A Gaggle of Resources for Open-Domain Question Answering
• Pre-Processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering
• From Baseline to Top Performer: A Reproducibility Study of Approaches at the TREC 2021 Conversational Assistance Track
• Demonstration Papers
• Exploring Tabular Data Through Networks
• InfEval: Application for Object Detection Analysis
• The System for Efficient Indexing and Search in the Large Archives of Scanned Historical Documents
• Public News Archive: A Searchable Sub-Archive to Portuguese Past News Articles
• TweetStream2Story: Narrative Extraction from Tweets in Real Time
• SimpleRad: patient-friendly Dutch radiology reports
• Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data
• Continuous Integration for Reproducible Shared Tasks with TIRA.io
• Dynamic Exploratory Search for the Information Retrieval Anthology
• Text2Storyline: Generating Enriched Storylines From Text
• Uptrendz: API-Centric Real-Time Recommendations in Multi-Domain Settings
• Clustering Without Knowing How To: Application and Evaluation
• Enticing local governments to produce FAIR freedom of information act dossiers
• Which Country is this? Automatic Country Ranking of StreetView Images
• Automatic Videography Generation from Audio Tracks
• Ablesbarkeitsmesser: A System for Assessing the Readability of German Text
• FACADE: Fake Articles Classification And Decision Explanation
• PsyProf: A Platform for Assisted Screening of Depression in Social Media
• SOPalign: A Tool for Automatic Estimation of Compliance with Medical Guidelines
• Tutorials
• Understanding and Mitigating Gender Bias in Information Retrieval Systems
• Neuro-Symbolic Representations for Information Retrieval
• Legal IR and NLP: the History, Challenges, and State-of-the-Art
• Deep Learning Methods for Query Auto Completion
• Trends and Overview: The Potential of Conversational Agents in Digital Health
• Crowdsourcing for Information Retrieval
• Uncertainty Quantification for Text Classification
• Workshops
• Fourth International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2023)
• The 6th International Workshop on Narrative Extraction from Texts (Text2Story'23)
• 2nd Workshop on Augmented Intelligence in Technology-Assisted Review Systems (ALTARS): Evaluation Metrics and Protocols for eDiscovery and Systematic Review Systems
• Workshop QPP++ 2023: Query Performance Prediction and Ist Evaluation in New Tasks
• Bibliometric-enhanced Information Retrieval: 13th International BIR Workshop (BIR2023)
• Geographic information extraction from texts (GeoExT)
• ROMCIR 2023: Overview of the 3rd Workshop on Reducing Online Misinformation through Credible Information Retrieval
• ECIR 2023 workshop proposal: Legal Information Retrieval
• Doctoral Consoritum
• Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing
• Text Information Retrieval in Tetun
• Identifying and Representing Knowledge Delta in Scientific Literature
• Investigation of Bias in Web Search Queries
• Monitoring online discussions and responses to support the identification of misinformation
• User Privacy in Recommender Systems
• Conversational Search for Multimedia Archives
• Disinformation Detection: Knowledge Infusion with Transfer Learning and Visualizations
• A Comprehensive Overview of Consumer Conflicts on Social Media
• Designing useful conversational interfaces for information retrieval in career decision-making support
• CLEF Lab Descriptions iDPP@CLEF 2023: The Intelligent Disease Progression Prediction Challenge
• LongEval: Longitudinal Evaluation of Model Performance at CLEF 2023
• The CLEF-2023 CheckThat! Lab: Checkworthiness, Subjectivity, Political Bias, Factuality, and Authority
• Overview of PAN 2023: Authorship Verification, Multi-Author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection
• Overview of Touché 2023: Argument and Causal Retrieval
• CLEF 2023 SimpleText Track: What Happens if General Users Search Scientific Texts?
• Science for Fun: The CLEF 2023 JOKER Track on Automatic Wordplay Analysis
• ImageCLEF 2023 Highlight: Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications
• LifeCLEF 2023 teaser: Species Identification and Prediction Challenges
• BioASQ at CLEF2023: The eleventh edition of the Large-scale biomedical semantic indexing and question answering challenge
• eRisk 2023: Depression, Pathological Gambling, and Eating Disorder Challenges
• Overview of EXIST 2023: sEXism Identification in Social neTworks
• DocILE 2023 Teaser: Document Information Localization and Extraction.

The three-volume set LNCS 13980, 13981 and 13982 constitutes the refereed proceedings of the 45th European Conference on IR Research, ECIR 2023, held in Dublin, Ireland, during April 2-6, 2023. The 65 full papers, 41 short papers, 19 demonstration papers, 12 reproducibility papers consortium papers, 7 tutorial papers, and 10 doctorial consortium papers were carefully reviewed and selected from 489 submissions. The book also contains, 8 workshop summaries and 13 CLEF Lab descriptions. The accepted papers cover the state of the art in information retrieval focusing on user aspects, system and foundational aspects, machine learning, applications, evaluation, new social and technical challenges, and other topics of direct or indirect relevance to search.

• Contextual Word Embeddings Clustering through Multiway Analysis: A Comparative Study
• Transferable Deep Metric Learning for Clustering
• Spatial Graph Convolution Neural Networks for Water Distribution Systems
• Data-Centric Perspective on Explainability versus Performance Trade-off
• Towards Data Science Design Patterns
• Diverse Paraphrasing with Insertion Models for Few-Shot Intent Detection
• LEMON: Alternative Sampling for More Faithful Explanation through Local Surrogate Models
• GASTeN: Generative Adversarial Stress Test Networks
• Learning Permutation-Invariant Embeddings for Description Logic Concepts
• Diffusion Transport Alignment
• Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
• Effects of Locality and Rule Language on Explanations for Knowledge Graph Embeddings
• Shapley Values with Uncertain Value Functions. -Revised Conditional t-SNE: Looking Beyond the Nearest Neighbors
• On the Change of Decision Boundary and Loss in Learning with Concept Drift
• AID4HAI: Automatic Idea Detection for Healthcare-Associated Infections from Twitter, A Framework based on Active Learning and Transfer Learning
• Explanations for Itemset Mining by Constraint Programming: A Case Study using ChEMBL data
• Translated Texts Under the Lens: From Machine Translation Detection to Source Language Identification
• Geolet: An Interpretable Model for Trajectory Classification
• An investigation of structures responsible for gender bias in BERT and DistilBERT
• Discovering diverse top-k characteristic lists
• Online Influence Forest for Streaming Anomaly Detection
• APs: a proxemic framework for social media interactions modeling and analysis
• User Authentication via Multifaceted Mouse Movementsand Outlier Exposure
• Explaining Black Box Reinforcement Learning Agents Through Counterfactual Policies
• A GNN-based Architecture for Group Detection from spatio-temporal Trajectory Data
• Discovering Rule Lists with Preferred Variables
• Don’t Start Your Data Labeling from Scratch: OpSaLa - Optimized Data Sampling Before Labeling
• The Other Side of Compression: Measuring Bias in Pruned Transformers
• Dropping incomplete records is (not so) straightforward
• Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets
• Should We Consider On-Demand Analysis in Scale-Free Networks?
• ROCKAD: Transferring ROCKET to whole time series anomaly detection
• Out-of-Distribution Generalisation with Symmetry-Based Disentangled Representations
• Forecasting Electricity Prices: an Optimize then Predict-based approach
• A Similarity-Guided Framework for Error-Driven Discovery of Patient Neighbourhoods in EMA Data
• QBERT: Generalist Model for Processing Questions
• On Compositionality in Data Embedding.

This book constitutes the proceedings of the 21st International Symposium on Intelligent Data Analysis, IDA 2022, which was held in Louvain-la-Neuve, Belgium, during April 12-14, 2023. The 38 papers included in this book were carefully reviewed and selected from 91 submissions. IDA is an international symposium presenting advances in the intelligent analysis of data. Distinguishing characteristics of IDA are its focus on novel, inspiring ideas, its focus on research, and its relatively small scale. .

• Intro
• General Chairs' Preface
• PC Chairs' Preface
• Organization
• Contents - Part II
• Graphs and Networks
• Improving Knowledge Graph Entity Alignment with Graph Augmentation
• 1 Introduction
• 2 Related Works
• 3 Preliminaries
• 4 Methodology
• 4.1 Entity-Relation Encoder
• 4.2 Model Training with Graph Augmentation
• 4.3 Alignment Inference
• 5 Experimental Setup
• 5.1 Experimental Setup
• 5.2 Experimental Results
• 6 Discussion and Conclusion
• References
• MixER: MLP-Mixer Knowledge Graph Embedding for Capturing Rich Entity-Relation Interactions in Link Prediction
• 1 Introduction
• 2 Related Work
• 2.1 Translation-Based Approaches
• 2.2 Matrix Factorization-Based Approaches
• 2.3 Neural Network-Based Approaches
• 3 Methodology
• 3.1 Problem Formulation and Notations
• 3.2 Overall Architecture Design
• 3.3 Model Architecture
• 4 Experiments
• 4.1 Datasets
• 4.2 Evaluation Protocol and Metric
• 4.3 Hyperparameters and Baselines
• 4.4 Results and Discussion
• 4.5 Analysis
• 5 Conclusion and Future Work
• References
• GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation
• 1 Introduction
• 2 Related Works
• 2.1 Temporal Dynamics Modeling on Graph-Structured Data
• 2.2 Representation Learning on Graphs with Edge Features
• 3 Proposed Methods
• 3.1 Problem Formulation
• 3.2 Overview of GTEA
• 3.3 Learning Edge Embeddings for Interaction Sequences
• 3.4 Representation Learning with Temporal Edge Aggregation
• 3.5 Model Training for Different Graph-Related Tasks
• 4 Experiments
• 4.1 Experimental Setup
• 4.2 Experimental Results of Overall Performance
• 4.3 Experiments Analyses
• 5 Conclusions
• References
• You Need to Look Globally: Discovering Representative Topology Structures to Enhance Graph Neural Network
• 1 Introduction.

• 2 Related Works
• 3 Problem Formulation
• 4 Methodology
• 4.1 Global Topology Structure Extraction
• 4.2 Graph Structure Memory Augmented Representation Learning
• 4.3 Objective Function of GSM-GNN
• 5 Experiments
• 5.1 Datasets
• 5.2 Experimental Setup
• 5.3 Performance on Node Classification
• 5.4 Flexibility of GSM-GNN for Various GNNs
• 5.5 Ablation Study
• 6 Conclusion
• References
• UPGAT: Uncertainty-Aware Pseudo-neighbor Augmented Knowledge Graph Attention Network
• 1 Introduction
• 2 Preliminaries
• 2.1 Problem Statement
• 2.2 Motivations and Challenges
• 2.3 Related Work
• 3 Approach
• 3.1 Overview
• 3.2 1-Hop Attention Module with Attention Baseline Mechanism
• 3.3 Confidence Score Prediction and Training Objective
• 3.4 Pseudo-neighbor Augmented Graph Attention Network
• 4 Experiment
• 4.1 Settings
• 4.2 Results and Analysis
• 4.3 Ablation Study
• 4.4 Deterministic Settings
• 5 Conclusion and Future Work
• References
• Mining Frequent Sequential Subgraph Evolutions in Dynamic Attributed Graphs
• 1 Introduction
• 2 Related Work
• 3 Notations
• 3.1 Dynamic Attributed Graph
• 3.2 A New Pattern Domain
• 3.3 Interesting Measures and Constraints
• 4 Mining Frequent Sequential Subgraph Evolutions
• 4.1 Extraction of Subgraph Candidates
• 4.2 Generation of Size-1 Patterns by Graph Addition
• 4.3 Extension of Patterns
• 5 Experiments
• 6 Conclusion
• References
• CondTraj-GAN: Conditional Sequential GAN for Generating Synthetic Vehicle Trajectories
• 1 Introduction
• 2 Problem Definition
• 3 The CondTraj-GAN Framework
• 3.1 Training
• 3.2 Trajectory Inference
• 4 Evaluation Setup
• 4.1 Dataset
• 4.2 Model Setups
• 4.3 Evaluation Metrics
• 4.4 Baselines
• 5 Evaluation
• 5.1 Trajectory Generation Performance
• 5.2 Ablation Study
• 6 Related Work
• 7 Conclusion and Future Work
• References.

• A Graph Contrastive Learning Framework with Adaptive Augmentation and Encoding for Unaligned Views
• 1 Introduction
• 2 Related Work
• 2.1 Graph Contrastive Learning
• 2.2 Adversarial Training
• 3 Method
• 3.1 Preliminaries
• 3.2 Adaptive Augmentation
• 3.3 Encoding Methods for Homophilic and Heterophilic Graphs
• 3.4 G-EMD-based Contrastive Loss
• 3.5 Adversarial Training on GCAUV
• 4 Experiments
• 4.1 Experimental Setup
• 4.2 Performance on Node Classification
• 4.3 Ablation Studies
• 5 Conclusion
• References
• MPool: Motif-Based Graph Pooling
• 1 Introduction
• 2 Related Work
• 3 Methodology
• 3.1 Preliminaries and Problem Formulation
• 3.2 Motif Based Graph Pooling Models
• 3.3 Readout Function and Output Layer
• 4 Experiment
• 4.1 Overall Evaluation
• 5 Conclusion
• References
• Anti-Money Laundering in Cryptocurrency via Multi-Relational Graph Neural Network
• 1 Introduction
• 2 Methodology
• 2.1 Graph Construction
• 2.2 Representation Embedding
• 2.3 Inter-relation Aggregation
• 2.4 Adaptive Neighbor Sampler
• 3 Experiment
• 3.1 Experimental Setup
• 3.2 Demystifying Mixing Behavior
• 3.3 Performance Comparison
• 3.4 Ablation Study
• 3.5 Adaptive Sampler Analysis
• 4 Conclusion
• References
• Interpretability and Explainability
• CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data Using Normalizing Flows
• 1 Introduction
• 2 Related Works
• 3 Preliminaries
• 3.1 Counterfactual Explanation
• 3.2 Normalizing Flow
• 4 Methodology
• 4.1 General Architecture of CeFlow
• 4.2 Normalizing Flows for Categorical Features
• 4.3 Conditional Flow Gaussian Mixture Model for Tabular Data
• 4.4 Counterfactual Generation Step
• 5 Experiments
• 6 Conclusion
• References.

• Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out
• 1 Introduction
• 2 Related Work
• 3 Data Collection
• 3.1 Study 1: Pre-event Control Period
• 3.2 Study 2: Post-event New User Period
• 4 Feedback Effect on Engagement
• 4.1 Covariates and Outcome Variables
• 4.2 Observational Causal Methods
• 4.3 Time to Next Engagement
• 4.4 Number of Active Days
• 5 Language Convergence in New User Cohort
• 5.1 New and Existing User Cohort Definition
• 5.2 New User's Self-Selection: Drop-out or Adaption
• 6 Discussions
• References
• Toward Interpretable Machine Learning: Constructing Polynomial Models Based on Feature Interaction Trees
• 1 Introduction
• 2 Related Work
• 2.1 SHAP and Pair-Wise Interaction Values
• 2.2 Polynomial Model and EBM
• 3 Methodology
• 3.1 Black-box Model Creation
• 3.2 Global SHAP Interaction Value Score Calculation
• 3.3 Tree-building Process
• 4 Experiments
• 4.1 Model Performance
• 4.2 Evaluating Interpretability
• 4.3 Usability Study
• 5 Conclusion
• References
• Kernel Methods
• BioSequence2Vec: Efficient Embedding Generation for Biological Sequences
• 1 Introduction
• 2 Related Work
• 3 Proposed Approach
• 3.1 BioSequence2Vec Representation
• 4 Experimental Evaluation
• 5 Results and Discussion
• 6 Conclusion
• References
• Matrices and Tensors
• Relations Between Adjacency and Modularity Graph Partitioning
• 1 Introduction
• 2 Preliminaries
• 3 Dominant Eigenvectors of Modularity and Adjacency Matrices
• 4 Normalized Adjacency and Modularity Clustering
• 5 Experiments
• 5.1 Synthetic Data Sets
• 5.2 PenDigit Data Sets from MNIST Database
• 6 Conclusion
• References
• Model Selection and Evaluation
• Bayesian Optimization over Mixed Type Inputs with Encoding Methods
• 1 Introduction
• 2 Related Work.

• 2.1 BO for Categorical and Continuous Inputs
• 2.2 Encoding Methods
• 3 Background
• 3.1 Problem Statement
• 3.2 Bayesian Optimization
• 4 The Proposed Framework
• 4.1 Target Mean Encoding BO
• 4.2 Aggregate Ordinal Encoding BO
• 5 Experiments
• 5.1 Baseline Method and Evaluation Measures
• 5.2 Performance and Computation Time
• 6 Conclusion
• References
• Online and Streaming Algorithms
• Using Flexible Memories to Reduce Catastrophic Forgetting
• 1 Introduction
• 2 Related Work
• 3 The Continual Learning Problem
• 4 The Stability Wrapper (SW) for Replay Buffer Replacements
• 5 Experimental Results
• 6 Conclusion
• References
• Fair Healthcare Rationing to Maximize Dynamic Utilities
• 1 Introduction
• 1.1 Our Models
• 1.2 Our Contributions
• 2 Algorithms for Model 1
• 2.1 Online Algorithm for Model 1
• 2.2 Charging Scheme
• 2.3 Tight Example for the Online Algorithm
• 3 Online Algorithm for Model 2
• 3.1 Outline of the Charging Scheme
• 4 Strategy-Proofness of the Online Algorithm
• 5 Experimental Evaluation
• 5.1 Methodology
• 5.2 Datasets
• 5.3 Results and Discussions
• 6 Conclusion
• References
• A Multi-player MAB Approach for Distributed Selection Problems
• 1 Introduction
• 2 Related Work
• 3 Platform Model and Problem Formulation
• 4 The Offline Optimization Problem
• 5 Online Learning Algorithm
• 6 Experiment
• 7 Conclusion
• References
• A Thompson Sampling Approach to Unifying Causal Inference and Bandit Learning
• 1 Introduction
• 2 Model
• 2.1 The Bandit Learning Model
• 2.2 The Data Model
• 2.3 Problem Formulation
• 3 Limitations of Naively Applying Thompson Sampling
• 3.1 VirTS: Naively Applying Thompson Sampling
• 3.2 Limitations of VirTS
• 4 VirTS-DF: Improving VirTS via Offline Data Filtering
• 5 Experiments on Real-world Data
• 5.1 Experimental Settings.

The 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25-28, 2023. The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.

• Intro
• General Chairs' Preface
• PC Chairs' Preface
• Organization
• Contents - Part II
• Graphs and Networks
• Improving Knowledge Graph Entity Alignment with Graph Augmentation
• 1 Introduction
• 2 Related Works
• 3 Preliminaries
• 4 Methodology
• 4.1 Entity-Relation Encoder
• 4.2 Model Training with Graph Augmentation
• 4.3 Alignment Inference
• 5 Experimental Setup
• 5.1 Experimental Setup
• 5.2 Experimental Results
• 6 Discussion and Conclusion
• References
• MixER: MLP-Mixer Knowledge Graph Embedding for Capturing Rich Entity-Relation Interactions in Link Prediction

• 1 Introduction
• 2 Related Work
• 2.1 Translation-Based Approaches
• 2.2 Matrix Factorization-Based Approaches
• 2.3 Neural Network-Based Approaches
• 3 Methodology
• 3.1 Problem Formulation and Notations
• 3.2 Overall Architecture Design
• 3.3 Model Architecture
• 4 Experiments
• 4.1 Datasets
• 4.2 Evaluation Protocol and Metric
• 4.3 Hyperparameters and Baselines
• 4.4 Results and Discussion
• 4.5 Analysis
• 5 Conclusion and Future Work
• References
• GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation
• 1 Introduction

• 2 Related Works
• 2.1 Temporal Dynamics Modeling on Graph-Structured Data
• 2.2 Representation Learning on Graphs with Edge Features
• 3 Proposed Methods
• 3.1 Problem Formulation
• 3.2 Overview of GTEA
• 3.3 Learning Edge Embeddings for Interaction Sequences
• 3.4 Representation Learning with Temporal Edge Aggregation
• 3.5 Model Training for Different Graph-Related Tasks
• 4 Experiments
• 4.1 Experimental Setup
• 4.2 Experimental Results of Overall Performance
• 4.3 Experiments Analyses
• 5 Conclusions
• References

• You Need to Look Globally: Discovering Representative Topology Structures to Enhance Graph Neural Network
• 1 Introduction
• 2 Related Works
• 3 Problem Formulation
• 4 Methodology
• 4.1 Global Topology Structure Extraction
• 4.2 Graph Structure Memory Augmented Representation Learning
• 4.3 Objective Function of GSM-GNN
• 5 Experiments
• 5.1 Datasets
• 5.2 Experimental Setup
• 5.3 Performance on Node Classification
• 5.4 Flexibility of GSM-GNN for Various GNNs
• 5.5 Ablation Study
• 6 Conclusion
• References

• UPGAT: Uncertainty-Aware Pseudo-neighbor Augmented Knowledge Graph Attention Network
• 1 Introduction
• 2 Preliminaries
• 2.1 Problem Statement
• 2.2 Motivations and Challenges
• 2.3 Related Work
• 3 Approach
• 3.1 Overview
• 3.2 1-Hop Attention Module with Attention Baseline Mechanism
• 3.3 Confidence Score Prediction and Training Objective
• 3.4 Pseudo-neighbor Augmented Graph Attention Network
• 4 Experiment
• 4.1 Settings
• 4.2 Results and Analysis
• 4.3 Ablation Study
• 4.4 Deterministic Settings
• 5 Conclusion and Future Work
• References

The 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25-28, 2023. The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.

• Big data
• Toward Explainable Recommendation Via Counterfactual Reasoning
• Online Volume Optimization for Notifications via Long Short-Term Value Modeling
• Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases
• Financial data
• Joint Latent Topic Discovery and Expectation Modeling for Financial Markets
• Let the model make financial senses: a Text2Text generative approach for financial complaint identification
• Information retrieval and search
• Web-scale Semantic Product Search With Large Language Models
• Multi-task learning based Keywords weighted Siamese Model for semantic retrieval
• Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion
• MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval
• Isotropic Representation Can Improve Dense Retrieval
• Knowledge-Enhanced Prototypical Network with Structural Semantics for Few-Shot Relation Classification
• Internet of Things
• MIDFA : Memory-Based Instance Division and Feature Aggregation Network for Video Object Detection
• Medical and biological data
• Vision Transformers for Small Histological Datasets learned through Knowledge Distillation
• Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis
• DKFM: Dual Knowledge-guided Fusion Model for Drug Recommendation
• Hierarchical Graph Neural Network for Patient Treatment Preference Prediction with External Knowledge
• Multimedia and multimodal data
• An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance
• Dynamically-Scaled Deep Canonical Correlation Analysis
• TCR: Short Video Title Generation and Cover Selection with Attention Refinement
• ItrievalKD: An Iterative Retrieval Framework Assisted with Knowledge Distillation for Noisy Text-to-Image Retrieval
• Recommender systems
• Semantic Relation Transfer for Non-overlapped Cross-domain Recommendations
• Interest Driven Graph Structure Learning for Session-Based Recommendation
• Multi-behavior Guided Temporal Graph Attention Network for Recommendation
• Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation
• Meta-learning Enhanced Next POI Recommendation by Leveraging Check-ins from Auxiliary Cities
• Global-Aware External Attention Deep Model for Sequential Recommendation
• Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking
• Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation
• kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval
• Staying or Leaving: A Knowledge-Enhanced User Simulator for Reinforcement Learning Based Short Video Recommendation
• RLMixer: A Reinforcement Learning Approach For Integrated Ranking With Contrastive User Preference Modeling.

The 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25-28, 2023. The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.

• Intro
• General Chairs' Preface
• PC Chairs' Preface
• Organization
• Contents - Part IV
• Scientific Data
• Inline Citation Classification Using Peripheral Context and Time-Evolving Augmentation*-12pt
• 1 Introduction
• 2 Related Work
• 3 Methodology
• 3.1 Cross-Text Attention
• 3.2 Spatial Fusion
• 3.3 Time Evolving Augmentation
• 4 Experiments
• 4.1 Dataset
• 4.2 Implementation Details
• 5 Baselines
• 6 Analysis
• 7 Conclusion
• References
• Social Network Analysis
• Post-it: Augmented Reality Based Group Recommendation with Item Replacement
• 1 Introduction

• 2 Problem Formulation
• 3 STAR3
• 3.1 Interaction- and Preference-Aware Graph Attention Network
• 3.2 Haptic-Aware Virtual Candidate Item Generator
• 3.3 Social- and Haptic-Aware Recommender
• 3.4 Overall Objective
• 4 Experiments
• 5 Conclusion
• References
• Proactive Rumor Control: When Impression Counts
• 1 Introduction
• 2 Related Work
• 3 Problem Formulation
• 3.1 Influence Model
• 3.2 Influence Block
• 3.3 Problem Definition
• 4 Our Framework
• 4.1 A Baseline
• 4.2 Branch-and-Bound Framework
• 4.3 Computing Upper Bound
• 4.4 Analysis of Solutions

• 5 Progressive Branch-and-Bound
• 6 Experiments
• 6.1 Experimental Settings
• 6.2 Effectiveness Test
• 6.3 Efficiency Test
• 6.4 Scalability Test
• 7 Conclusion
• References
• Spatio-Temporal Data
• Generative-Contrastive-Attentive Spatial-Temporal Network for Traffic Data Imputation
• 1 Introduction
• 2 Related Work
• 3 Preliminaries
• 4 The GCASTN Model
• 4.1 Generative-Contrastive Self-Supervised Learning
• 4.2 Data Augmentation via Two-Fold Cross Random Masking
• 4.3 GCASTN Encoder
• 4.4 GCASTN Decoder
• 5 Experiments
• 5.1 Datasets and Baselines
• 5.2 Experimental Results

• 6 Conclusion
• References
• Road Network Representation Learning with Vehicle Trajectories*-12pt
• 1 Introduction
• 2 Problem Definition
• 3 TrajRNE Approach
• 3.1 Spatial Flow Convolution
• 3.2 Structural Road Encoder
• 3.3 TrajRNE Overview
• 4 Experimental Evaluation
• 4.1 Datasets
• 4.2 Baselines
• 4.3 Downstream Tasks and Evaluation Metrics
• 4.4 Experimental Settings
• 4.5 Performance Results
• 4.6 Ablation Study
• 4.7 Parameter Study
• 5 Related Work
• 6 Conclusion
• References
• MetaCitta: Deep Meta-Learning for Spatio-Temporal Prediction Across Cities and Tasks*-12pt

• 1 Introduction
• 2 Problem Statement
• 3 The MetaCitta Approach
• 3.1 Spatial Encoder
• 3.2 Temporal Encoder
• 3.3 Prediction
• 3.4 Training Procedure
• 4 Evaluation Setup
• 4.1 Datasets
• 4.2 Baselines
• 4.3 Experimental Settings
• 5 Evaluation
• 5.1 Comparison with Baselines
• 5.2 Ablation Study
• 5.3 Training Time Comparison
• 6 Related Work
• 7 Conclusion
• References
• Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems
• 1 Introduction
• 2 Preliminaries
• 2.1 Definitions
• 2.2 Problem Statement
• 3 Methodology
• 3.1 Framework Overview

The 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25-28, 2023. The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.

• PAAMS 2023 - MAIN TRACK
• Brain waves classification using a single-channel dry EEG headset: an application for controlling an intelligent wheelchair
• AJAN: An Engineering Framework for Semantic Web-Enabled Agents and Multi-Agent Systems
• Adaptive consumption by continuous negotiation
• When Extrinsic Payoffs meet Intrinsic Expectations
• Multi-Agent Reinforcement Learning for Structured Symbolic Music Generation
• A multi-level density-based crowd simulation architecture
• Resolving the Dilemma of Responsibility in Multi-Agent Flow Networks
• Lessons Learned on the Design of a Predictive Agent for LoRaWAN Network Planning
• A solution concept with an exploration bias for repeated stochastic coalitional games
• Governing online forum interactions with multi-agent system: A Reddit use case with the JaCaMo platform
• Fuzzy Hyperplane based Twin-KSVC and Its Applications to Stock Price Movement Direction Prediction Based on News Articles
• Enhancing Safety Checking Coverage with Multi-swarm Particle Swarm Optimization
• A Decentralized Resource Allocation Protocol in Social Networks
• Multi-agent learning of causal networks in the Internet of Things
• Investigation of Integrating Solution Techniques for Lifelong MAPD Problem Considering Endpoints
• Towards Norm Entrepreneurship in Agent Societies
• Velluscinum: A Middleware for Using Digital Assets in Multi-Agent Systems
• Parking Problem with Multiple Gates
• UAV Swarm Control and Coordination Using Jason BDI Agents on top of ROS
• Traffic Assignment Optimization using Flow-based Multi-Maps
• A Monitoring Agent for Advancing Elderly Care through Mobile Health Technology
• Extrinsic Emotion Regulation by Intelligent Agents: A computational model based on Arousal-Valence dimensions
• Optimization of Complex Systems in Photonics by Multi-Agent Robotic Control
• GTG-CoL: A new Decentralized Federated Learning based on Consenssus for dynamic networks
• A novel framework for multi-agent knowledge-based federated learning systems
• Exploring Planner-Guided Swarms Running on Real Robots
• Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents
• A Multi-Agent Based Dynamic Network Slice Tarification Framework
• An IDE to Support the Development of Embedded Multi-Agent Systems. Bioinspired Artificial Cockroach Colony Strategy combined with 2-type Fuzzy Logic for the Priority-Based Sanitization of Railway Stations
• Embedding Autonomous Agents into Low-Power Wireless Sensor Networks
• Safe Autonomous Decision-Making with vGOAL
• PAAMS 2023 - DEMONSTRATIONS
• Towards Exception Handling in the SARL Agent Platform
• Multi-Agent Path Finding for Indoor Quadcopters
• Energy community integration of a smart home based on an open source multiagent system
• Cognitive Assistant for Enhanced eHealth Monitoring
• Extended Green Cloud - Modeling Cloud Infrastructure with Green Energy Sources

This book constitutes the proceedings of the 21st International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2023, held in Guimaraes, Portugal, in July 2023. The 32 full papers in this book were reviewed and selected from 70 submissions. 5 demonstration papers are also included in this volume. The papers deal with the application and validation of agent-based models, methods, and technologies in a number of key applications areas, including: advanced models and learning, agent-based programming, decision-making, education and social interactions, formal and theoretic models, health and safety, mobility and the city, swarms and task allocation